CN116234614A - Field-by-field prediction by wearable devices - Google Patents

Abstract

The present invention relates to a system for providing a session-by-session prediction to users actually participating in a sporting event. The user has a wearable device for capturing game data and the local program calculates the probabilities of different event outcomes in the game based on the observed game and game situation. The user may predict through the wearable device interface. A sensor on the wearable device, such as a camera or microphone, is used to determine if the user's prediction is correct or incorrect.

Description

Field-by-field prediction by wearable devices

Technical Field

Embodiments relate generally to stadium-by-stadium sports prediction games played by mobile devices and wearable devices, and in particular, to prediction games played on sporting events in which a user is participating.

Background

The subject matter discussed in the background section should not be assumed to be prior art merely because it was mentioned in the background section. Also, the problems mentioned in the background or the problems associated with the subject of the background should not be assumed to have been recognized in the prior art. The subject matter in the background is only representative of various approaches, which may themselves correspond to implementations of the claimed technology.

The present invention relates to capturing game data and communicating it to a user's mobile device for a predicted game play by play, which may be problematic if the user is in a live game. The large number of people in a stadium makes obtaining reliable bandwidth problematic and this can cause the predictive game provider to lose potential users.

Disclosure of Invention

The invention relates to a method, a system and a device for predicting by using a wearable device. In one embodiment, a predictive game system may include: a history match database from which the frequency of each event outcome in the history match database can be screened against the intra-game background of each event; and a wearable device comprising a wearable device user interface and at least one sensor that captures intra-game information, wherein the at least one sensor on the wearable device captures intra-game data to determine a situation and context for a given event; and calculating probabilities of various outcomes of the next event from the historical game data and representing the probabilities as potential predictions on the wearable device user interface.

Another embodiment relates to a method of predicting on a live event, comprising: capturing data from a live event on one or more sensors of a wearable device; judging whether the captured data exceeds a threshold value; if the captured data exceeds the threshold, calculating the probability of the next event; displaying a predictive option for a next event on the wearable device; and accepting input to the prediction on the wearable device.

Drawings

The drawings illustrate various embodiments of the systems and methods of the present invention and various other aspects of embodiments. It will be appreciated by those of ordinary skill in the art that the element boundaries (e.g., blocks, groups of blocks, or other shapes) illustrated in the figures represent one example of boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another element, and vice versa. Furthermore, elements may not be drawn to scale. Non-limiting and non-exhaustive descriptions are made with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles.

Fig. 1 illustrates a system for field-by-field prediction by a wearable device, according to one embodiment.

Fig. 2 shows a basic module according to one embodiment.

FIG. 3 illustrates a data capture module according to one embodiment.

FIG. 4 illustrates a probability calculation module, according to one embodiment.

FIG. 5 illustrates a prediction module according to one embodiment.

Fig. 6 illustrates a points wallet database according to one embodiment.

FIG. 7 illustrates a data feed database according to one embodiment.

Detailed Description

Various aspects of the invention are disclosed in the following description of specific embodiments of the invention and the associated drawings. Those skilled in the art will recognize that alternative embodiments may be devised without departing from the spirit or scope of the claims. Furthermore, well-known elements of exemplary embodiments of the invention will not be described in detail or will be omitted so as not to obscure the relevant details of the invention.

The term "exemplary" as used herein refers to serving as an example, instance, or illustration. The embodiments described herein are not limiting, but merely exemplary. It should be understood that the described embodiments are not necessarily to be construed as preferred or advantageous over other embodiments. Furthermore, the terms "embodiments of the invention," "embodiments" or "invention" do not require that all embodiments of the invention include the discussed feature, advantage or mode of operation.

Moreover, many of the embodiments described herein are described in terms of sequences of actions to be performed by, for example, elements of a computing device. Those skilled in the art will recognize that the various sequences of actions described herein can be performed by specific circuits (e.g., application Specific Integrated Circuits (ASICs)) and/or program instructions executed by at least one processor. Furthermore, the sequence of actions described herein can be embodied entirely within any form of computer readable storage medium such that execution of the sequence of actions would enable a processor to perform the functions described herein. Thus, the various aspects of the invention may be embodied in a number of different forms, all of which are contemplated to be within the scope of the claimed subject matter. Moreover, for each embodiment described herein, the corresponding form of any such embodiment may be described herein, for example, as a computer configured to perform the described actions.

With respect to the examples, a brief description of the terms used herein is provided.

An action refers to a particular game or a particular sport in a sporting event. For example, an action may decide which athletes are engaged in a sporting event. In some embodiments, the action may be a throwing, shooting, passing, swinging, kicking, hitting performed by the participant in a sporting event. In some embodiments, the action may be a strategic decision made by a sporting event participant (e.g., athlete, coach, manager, etc.). In some embodiments, the action may be a penalty, or type of violation occurring in the sporting event. In some embodiments, the action may include a participant in a sporting event. In some embodiments, the action may include a start event for a sporting event, such as a start prompt, coin-out, start ball-in, national singer, and the like. In some embodiments, the sporting event may be a football, hockey, basketball, baseball, golf, tennis, soccer, cricket, rugby, MMA, boxing, swimming, skiing, snowboarding, horse racing, boating, bicycling, wrestling, olympic, electronic athletic, and the like. The actions can be integrated into the embodiments in a variety of ways.

Digital games refer to any type of electronic environment that can be controlled or manipulated by a human user for entertainment purposes. A system enables people and computers to interact within the electronic domain according to a set of implicit and explicit rules for entertainment or instruction. "electronic competition" refers to a form of athletic competition that uses video games, or multiplayer video games that are played competitively for spectators, typically by professional players. Digital games and "electronic games" can be integrated into embodiments in a variety of ways.

The term "event" refers to a form of competition, sport, contest or game, particularly according to rules and determined by skill, strength or chance. In some embodiments, the event may be a football, hockey, basketball, baseball, golf, tennis, soccer, cricket, rugby, MMA, boxing, swimming, skiing, snowboarding, horse racing, boating, bicycling, wrestling, olympic games, and the like. The event can be integrated into the embodiment in a variety of ways.

"total" refers to the total number of runs (run), scores, or goals of the two parties in the game, including the addition game. "exceeding" refers to a sports prediction in which a player predicts that the total score of two teams will exceed a specified sum. "below" means that the total score of two teams is predicted to be below a certain number. The "total," "exceeding," and "below" can be integrated into the embodiments in a variety of ways.

"listing pitchers" refers specifically to a prediction of baseball that is only made if two pitchers planning to start a game do start. If they do not start, the prediction will be considered "no action" and cancelled. "column-defined hands" and "no action" can be integrated into an embodiment in a variety of ways.

A customer is a company, organization, or individual who will deploy, and may be part of, or perform, the various system elements or method steps of the embodiments.

The management service user interface service is such a service: which can help customers (1) manage third parties; (2) developing a web page; (3) performing data analysis; (4) connecting through an application program interface; and (4) track and report player behavior. The management service user interface can be integrated into an embodiment in a variety of ways.

The management service risk management service is a service such as: which helps the customer (1) to manage very important personnel; (2) implementing business intelligence; and (3) reporting. These management services risk management services can be integrated into embodiments in a variety of ways.

The management service compliance service is a service such as: which helps customer management (1) integrity monitoring; (2) race safety; and (3) customer service assistance. These management service compliance services can be integrated into an embodiment in a variety of ways.

The management service transaction service is a service that helps customers in the following aspects: (1) an official data feed; (2) data visualization; and (3) property based digital signage. These management services transaction services can be integrated into embodiments in a variety of ways.

The management services and technology platform is a service that helps customers to conduct (1) web hosting, (2) IT support, and (3) player account platform support. These management services and technology platform services can be integrated into embodiments in a variety of ways.

"custom prediction" allows a customer to customize a personalized prediction experience through complex tracking and analysis of player behavior. "custom prediction" can be integrated into an embodiment in a variety of ways.

Kiosk (Kiosk) is a device that provides interaction with customers, clients and users, providing a variety of modular solutions for retail and online sporting events. Kiosk can be integrated into the embodiments in a variety of ways.

Business applications are integrated kits for customer management to push sales, profits, and growing daily activities, helping customers manage sporting events from creating and expressing executable insight into performance. Business applications can be integrated into embodiments in a variety of ways.

The tournament configurator allows the customer operator to configure to have the opportunity to apply various selected or newly created business rules in the tournament and parameterize game management. The race configurator can be integrated into the embodiments in a variety of ways.

A "fantasy movement connector" is a software connector between method steps or system elements in an embodiment, and may be integrated with fantasy movement. Fantasy sports allow such a competition: wherein participants select fictional teams from among the players in the league and score according to their player's actual performance. For example, if a player in phantom sports is playing in a given real-time sport, the probability for that player in the real-time sport may change.

Software as a service (or SaaS) is a method of software delivery and licensing in which software is accessed online through subscription, rather than purchasing and installing the software on a personal computer. Software, i.e. services, can be integrated into the embodiments in a variety of ways.

Screen synchronization refers to synchronizing predictions and results between devices such as televisions and cell phones, personal Computers (PCs) and wearable devices. Screen synchronization can be integrated into an embodiment in a variety of ways.

Automatic Content Recognition (ACR) is an identification technique for identifying content played on a media device or content present in a media file. Devices incorporating ACR support enable users to quickly obtain additional information about what they see without any user-based input or search effort. To begin identification, a short media clip (audio, video, or both) is selected. This clip may be selected from within the media file or recorded by the device. Information is obtained from the actual perceived content by means of algorithms such as fingerprints and is compared with a database of reference fingerprints, each corresponding to a known record. The database may contain metadata and related information about the work, including supplemental media. If the fingerprints of the media clips are matching, the identification software returns the corresponding metadata to the client application. For example, in an ongoing sports game, a "ball loss" may be identified and metadata such as "ball loss" may be displayed at the time stamp of the event. Automatic Content Recognition (ACR) can be integrated into an embodiment in a variety of ways.

Joining social media refers to connecting ongoing sports predictions or results to a social media connection, e.g

Chat interactions. Joining social media can be integrated into an embodiment in a variety of ways.

Augmented reality refers to a technique of superimposing a computer-generated image on the real world in the user's eye, thereby providing a composite view. In an example of the present invention, a real-time view of the game may be seen and computer-generated data points, i.e., "predictions," placed over players associated with the predictions. The augmented reality can be integrated into the embodiments in a variety of ways.

Reference will now be made in detail to some embodiments of the invention, which embody all the features of the invention. It will be appreciated that the embodiments are intended to be open ended, as the use of one or more items in an embodiment is not meant to be an exhaustive list of such items, nor is it meant to be limited to only the listed items.

It should be noted that, as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments, only some exemplary systems and methods are now described.

The invention provides a system for field-by-field prediction through wearable equipment. In element 102, the system includes a live event 102, such as a sports event, for example, a football game, basketball game, baseball game, hockey game, tennis game, golf game, etc. The system includes a plurality of sensors 104 that may be used, such as motion sensors, temperature sensors, humidity sensors, cameras (e.g., RGB-D cameras, which are digital cameras that provide color (RGB) and depth information for each pixel in an image), microphones, radio frequency receivers, thermal imagers, radar devices, lidar devices, ultrasound devices, speakers, and the like. Further, the plurality of sensors 104 may include tracking devices, such as RFID tags, GPS chips, or other such devices embedded on uniforms, in devices, in a playing field, in a boundary of a playing field, or other indicia on a playing field. The imaging device may also be used as a tracking device in element 104, such as player tracking, which provides statistics through real-time X, Y positioning of the player and X, Y, Z positioning of the ball. The system also includes a cloud 106 or a communication network, which may be a wired and/or wireless network. Wireless communication networks using communication technologies include, but are not limited to, visible Light Communication (VLC), worldwide Interoperability for Microwave Access (WiMAX), long Term Evolution (LTE), wireless Local Area Network (WLAN), infrared (IR) communication. Wireless communication networks may allow ubiquitous access to shared pools of configurable system resources and higher-level services that can be provided quickly, typically through the internet, with minimal management effort, and rely on resource sharing for consistency and economies of scale. The wireless communication may also include a third party cloud that enables an organization to focus on its core traffic rather than expending resources on computer infrastructure and maintenance. The cloud may be communicatively connected to the server 108, and the server 108 may analyze the game type and game results in real time. The cloud may also be synchronized with game situation data such as game time, score, location on the scene, weather conditions, etc., which may affect the choice of game used. In other exemplary embodiments, the cloud may not receive data collected from the sensors, but may receive data from alternative data feeds such as sports radar. This data may be provided almost immediately after any game is over, and the data from this feed may be compared to various team data and tournament data, as described in various exemplary embodiments herein, depending on various factors, including attack (down), ball control rate, score, time, team, etc. The system may include a server 108 that may analyze the type of game and the results of the game or action in real time. The server 108 (or cloud 106) may also synchronize with game situation data such as game time, score, on-site location, weather conditions, etc., which may affect the choice of game used. For example, in other exemplary embodiments, the server 108 may not receive data collected from sensors, but rather data from alternative data feeds such as sports radar. In element 108, this data may be provided almost immediately after any game is over, and the data from this feed may be compared to various team data and tournament data, as described in various exemplary embodiments herein, based on various factors, including attack, ball control rate, score, time, team, etc. The user device is, for example, a computing device, a notebook computer, a smart phone, a tablet computer, a smart speaker, or an I/O device. The I/O device may reside in a computing device. Input devices may include keyboards, mice, trackpads, trackballs, touch pads, touch mice, multi-touch pads and touch mice, microphones, multi-array microphones, drawing pads, cameras, single lens reflex cameras (SLRs), digital SLRs (DSLRs), CMOS sensors, accelerometers, infrared optical sensors, pressure sensors, magnetometer sensors, angular rate sensors, depth sensors, proximity sensors, ambient light sensors, gyroscopic sensors, or other sensors. Output devices may include video displays, graphic displays, speakers, headphones, inkjet printers, laser printers, and 3D printers. The device may comprise a combination of multiple input or output devices, including, for example, KINECT by Microsoft corporation, wii mote by Wit, wii U GAMEPAD by Wit, or IPHONE by apple corporation. Some devices allow gestures to recognize inputs by combining some inputs and outputs. Some devices provide facial recognition that can be used as input for different purposes, including authentication and other commands. Some devices provide speech recognition and input, including, for example, a Kinect from Microsoft, a SIRI on IPHONE from apple, a NOW from Google, or a voice search from Google.

Other user devices have input and output functions, including, for example, a haptic feedback device, a touch screen display, or a multi-touch display. Touch screens, multi-touch displays, touch pads, touch mice, or other touch-sensing devices may use different technologies to sense touch, including, for example, capacitive, surface capacitive, projected Capacitive (PCT), embedded capacitive, resistive, infrared, waveguide, dispersive Signal Touch (DST), embedded optical, surface Acoustic Wave (SAW), bending Wave Touch (BWT) sensing technologies, or force-based sensing technologies. Some multi-touch devices may allow two or more points of contact to contact a surface, allowing advanced functions including, for example, pinching, expanding, rotating, scrolling, or other gestures. Some touch screen devices, including, for example, microsoft corporation's pixelse or Multi-Touch Collaboration Wall (Multi-touch collaborative wall), may have a large surface, for example, on a desktop or wall, and may also interact with other electronic devices. Some I/O devices, display devices, or groups of devices may be augmented reality devices. The I/O devices may be controlled by an I/O controller. The I/O controller may control one or more I/O devices, such as a keyboard and a pointing device, such as a mouse or a light pen. In addition, the I/O devices may also provide storage and/or installation media for the computing devices. In still other embodiments, the computing device may provide a USB connection (not shown) to receive a handheld USB storage device. In further embodiments, the I/O device may be a bridge between a system bus and an external communication bus, such as a USB bus, a SCSI bus, a FireWire bus, an Ethernet bus, a gigabit Ethernet bus, a fibre channel bus, or a Thunderbolt interface bus. In the present invention, the user device may be an optional component and in element 110 the user device will be used in a situation where the paired wearable device uses the user device as additional memory or computing power or as a connection to the internet. The interface may accept input from the user or provide output to the user, or both. In one case, a user may interact with the interface using one or more user interaction objects and devices. The user interaction objects and devices may include user input buttons, switches, knobs, levers, keys, trackballs, touch pads, cameras, microphones, motion sensors, thermal sensors, inertial sensors, touch sensors, or combinations thereof. Further, in element 112, the interface may be implemented as a Command Line Interface (CLI), a Graphical User Interface (GUI), a voice interface, or a web-based user interface. Wearable devices are a class of electronic devices that can be worn on the body as accessories, such as smart watches, or smart glasses, or devices embedded in clothing, or devices implanted in the body of a user, or devices with tattoos on the skin. These devices are hands-free devices that have practical use, are powered by microprocessors, and enhance the ability to send and receive data over the internet. In element 114, some examples of wearable devices include smart watches and smart glasses. The interface may accept input from the user or provide output to the user, or both. In one case, a user may interact with the interface using one or more user interaction objects and devices. In element 116, examples include a touch screen on a smart watch or a near-eye display on smart glasses. In element 118, the base module allows the user to log into the system, the reminder data capture module determines game and play information, the reminder probability calculation module calculates probabilities of various play results based on the context of the situation determined by the data capture module, the reminder prediction module collects predictions for the user, and again prompts the data capture module to collect play results, then compares these results with the predictions, and updates the wallet database based on the predicted results. In element 120, the data capture module determines which sensors on the wearable device are available, such as a microphone or video feed, determines which relevant data points can be captured by the available sensors, monitors these data points and returns game results based on the collected data. In element 122, a probability calculation module calculates a probability for each potential game outcome based on historical game data and game context associated with the team involved. In element 124, the prediction module displays available predictions to the user via the wearable interface, polls for predictions selected by the user, and returns the selections to the base module. In element 126, the points wallet database tracks the balance of points in the user account and updates the balance after each prediction. In element 128, the data feed database contains all the sensor types (in this example, microphones or video feeds) that the system can use from the wearable device, and which relevant data points each sensor type can capture in order to determine the information of the game being watched. In this embodiment, the history game database is located on the wearable device, but may also be located on the user device or cloud server based on storage space, processing power, and available bandwidth. In element 130, the database contains historical game data for the relevant sports. In element 132, sensors 1-n are sensors in the wearable device that may be used to collect information about the game, which may be used to determine the game outcome and context.

The function of the basic module will now be explained with reference to fig. 2. Those skilled in the art will recognize that for this and other processes and methods disclosed herein, the functions performed in these processes and methods can be implemented in a different order. Furthermore, the outlined steps and operations are provided as examples only, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

This figure shows the basic module. The base module begins in step 200 with a user logging into the system, typically through an application on his wearable device. In some embodiments, the base module may be located on the user's mobile device and the data capture and predictive interaction is done on a paired wearable device (such as a smart glasses or smart watch). In step 202, the base module first initiates a data capture module to determine which race the user is in, and then monitors the relevant data points to identify actions in the race. The game data is then returned from the data capture module. In step 204, the game data may be any sensor data indicative of any content about the real-time game, such as, but not limited to, audio or visual data indicative of "action," "both parties," "event," data, "total" data, "listed pitchers," specific athletes, whistles, crimes, touchdown scores, goals, numbers of goals, athlete errors, and the like. Then, in step 206, the base module activates the probability calculation module to calculate the probability of the next race. In step 208, the probability calculation model returns the probability of the potential outcome of the next game. Then, in step 210, the base module activates the prediction module, displaying the available predictions to the user, and collecting data about any predictions they have selected. The prediction module then returns prediction selection information. In step 212, the prediction selection information may be, for example, "prediction" or "probability". Then, in step 214, the base module again activates the data capture module to obtain game result information and compares it with the prediction choices returned by the prediction module. In step 216, game result information is received from the data capture module and information in the points wallet database is updated based on the prediction results. Then, in step 218, the module determines whether the game is over based on the previous game results. If the game is not over, return is made to step 202. If the game ends, the program ends. It should be noted that in step 220, the base module may be used for "customer" access, reconfiguration, modification or control, or for "management service user interface service", "management service risk management service", "management service compliance service", "management service transaction service", "management service and technology platform", "participation promotion", "business application", "competition configurator", "fantasy sports connector", "software as a service", "screen synchronization", "automatic content identification (ACR)", "joining social media", and "augmented reality".

The function of the data capture module will now be explained with reference to fig. 3. Those skilled in the art will recognize that for this and other processes and methods disclosed herein, the functions performed in these processes and methods can be implemented in a different order. Furthermore, the outlined steps and operations are provided as examples only, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

This figure shows the data capture module. The process begins with step 300 receiving a prompt from a base module. In step 302, the data capture module first determines which data feeds (e.g., microphone or video feeds) are available to the user's wearable device. In step 304, based on the data feeds from sensors 1-n on the user's wearable device, it is determined from the data feed database which relevant data points to search in the data feeds from each available sensor. For example, if the user's wearable device is a pair of smart glasses with both audio and video feeds, the system can either listen with a microphone to the referee's whistle indicating the end of the game, or rely on the video feed to see changes in the attack markers, or see where the referee lifts his arm indicating a new challenge line. Then, in step 306, the data capture module begins to poll the data feeds from the identified sensors in the wearable device to search for relevant data points. Then, in step 308, the data capture template determines whether a relevant data point has been received. When no relevant data points are received, the data capture model returns to polling in step 306. When the relevant data point is received, the data capture module determines whether additional data points are needed to determine the outcome of the game in step 310. For example, the microphone provides relevant data points for whistle that the referee indicates that the current game has ended. The data capture module will then require audio of the number of codes obtained by the public announcer's determination, or a video feed requiring the referee to raise his arm to indicate a new challenge line. Once enough relevant data points are received to identify the race, the race data is sent to the base module in step 312.

The function of the probability capture module will now be explained with reference to fig. 4. Those skilled in the art will recognize that for this and other processes and methods disclosed herein, the functions performed in these processes and methods can be implemented in a different order. Furthermore, the outlined steps and operations are provided as examples only, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

This figure shows the probability capture module. The process begins with the prompt of the base module in step 400. The probability calculation module will then retrieve all historical game data relating to the team in the current game from the historical game database in step 402. The probability calculation module then screens the retrieved matches for current context (e.g., which attack, current score, attack and distance, weather, etc.). In step 404, the remaining games are classified according to their frequency in the current context. In one example, the criteria of the present invention is to sort and screen football-specific games. These exemplary criteria will be specific to the sports item in question. Probabilities are assigned to the screened game results based on their historical frequency or one of the other methods of assigning probabilities known in the art. In step 406, the best probability is assigned to the most frequent results and the least frequently used results are assigned to the least frequently used results. In step 408, the game probabilities between the most frequent and least frequent are assigned in the game distribution between the best probability and the least frequently probability. The calculated probabilities are then sent to the base module in step 410.

The function of the prediction module will now be explained with reference to fig. 5. Those skilled in the art will recognize that for this and other processes and methods disclosed herein, the functions performed in these processes and methods can be implemented in a different order. Furthermore, the outlined steps and operations are provided as examples only, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

This figure shows the prediction module. The process begins with the prompt of the base module in step 500. Then in step 502, the prediction module queries the wearable device interface to retrieve configuration information. Configuration information received from the wearable device interface will decide how much predictions can be presented to the user, and in what format the predictions should be presented. The prediction module then displays a portion of the available predictions, in this example the prediction with the best probability, in step 504. In other embodiments, the predictions displayed may be based on other factors, such as user history or preferences. Then in step 506, the prediction module starts a timer. The timer will be customized according to the race cadence of a given race or sport. In the present embodiment, the timer is set to 20 seconds. This is because the sport in this example is American football. A 40 second game clock is used, which is a reasonable time to allow the user to consider predictions, but is not so long that it is predicted after the game begins. The predictions submitted after the game starts are not valid, but a timer is a way to allow the software to return to the data capture module. Once the timer is started, the module polls the wearable device interface in step 508 to determine the user's predictions. Then in step 510, the prediction module determines whether a prediction was received. If a prediction is received, the module returns the prediction information to the base module. If no prediction is received, then in step 512 the prediction module will poll a timer to determine if it has reached the threshold for motion. If the timer has not expired, the prediction module will return to polling the wearable interface to determine the prediction. Once the prediction is received or the timer has reached a predetermined threshold, the prediction module returns this information to the base module in step 514.

The functionality of the points wallet database will now be explained with reference to fig. 6. Those skilled in the art will recognize that for this and other processes and methods disclosed herein, the functions performed in these processes and methods can be implemented in a different order. Furthermore, the outlined steps and operations are provided as examples only, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

This figure shows the points wallet database. The database contains the balance of the actual points of the user, the predictions of each time the user, the results, and the new balance in the user's account. Each user has its own table in the database. The first column records the predicted game. The second column records predictions of the user, such as predicting that the next match will be a pass of more than 20 yards. The third column records the number of credits the user has placed in the field. The fourth column records the probability of user prediction, e.g. 2:1. the fifth column records whether the user's prediction was correct or incorrect. The sixth column records the user's account balance before the forecast. The seventh column contains the user's account balance after the predicted outcome is calculated in element 600.

The function of the data feed database will now be explained with reference to fig. 2. Those skilled in the art will recognize that for this and other processes and methods disclosed herein, the functions performed in these processes and methods can be implemented in a different order. Furthermore, the outlined steps and operations are provided as examples only, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

This figure shows a data feed database. The database contains the type of sensors that the system can use to collect information about the game, such as when the game is over and the results. The first column describes the sensor type, such as a microphone or a camera. The second column lists the first piece of relevant data that the sensor is looking for, such as whistle to the microphone. The third column contains the meaning of the relevant data point. For example, the referee's whistle will indicate to the microphone that the game has ended. In element 700, the two column pattern of related data points and their meanings is repeated for all related data points that each sensor can collect.

The foregoing description and drawings illustrate the principles, preferred embodiments and modes of operation of the present invention. However, the present invention should not be construed as being limited to the specific embodiments described above. Those skilled in the art will recognize additional variations of the embodiments described above.

The above embodiments are therefore to be regarded as illustrative rather than restrictive. It is therefore to be appreciated that variations may be made to these embodiments by those skilled in the art without departing from the scope of the invention as defined by the appended claims.

Claims (16)

1. A predictive game system comprising:

a history match database from which the frequency of each event outcome in the history match database can be screened against the intra-game background of each event; and

a wearable device comprising a wearable device user interface and at least one sensor that captures intra-game information, wherein the at least one sensor on the wearable device captures intra-game data to determine a situation and context for a given event; and

probabilities of various outcomes of the next event are calculated from the historical game data and expressed as potential predictions on the wearable device user interface.

2. The system of claim 1, wherein the at least one sensor comprises at least one of a microphone and a camera.

3. The system of claim 1, further comprising a data capture module that determines relevant data points on the wearable device that the at least one sensor is capable of capturing.

4. The system of claim 1, further comprising a mobile device communicatively paired with the wearable device.

5. The system of claim 4, wherein the wearable device is at least one of a smart glasses and a smart watch.

6. The system of claim 4, further comprising a base module on the mobile device that activates one or more sensors on the wearable device to capture data.

7. The system of claim 4, further comprising a probability computation module that generates a probability of a next result upon receipt of the captured data.

8. The system of claim 7, wherein the base module activates one or more sensors on the wearable device to capture data at the end of a next event.

9. The system of claim 6, wherein the base module queries the wearable device to determine a type of the at least one sensor and data that the at least one sensor is able to collect.

10. The system of claim 7, wherein the probability calculation module is enabled after a plurality of data points exceeding a threshold are determined to be received by the data capture module.

11. A method of making predictions on a live event, comprising:

capturing data from a live event on one or more sensors of a wearable device;

judging whether the captured data exceeds a threshold value;

if the captured data exceeds the threshold, calculating the probability of the next event;

displaying a predictive option for a next event on the wearable device; and

input to a prediction on the wearable device is accepted.

12. The method of claim 11, further comprising pairing the wearable device with a mobile device.

13. The method of claim 11, further comprising receiving a signal on the wearable device to begin capturing data about a next event and then capturing data about the next event.

14. The method of claim 13, wherein the signal is received from the mobile device.

15. The method of claim 13, further comprising comparing data captured from the live event with data in a historical database to calculate a probability of the next event.

16. The method of claim 11, further comprising determining whether a prediction was successful based on an analysis of data captured from the next event.

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